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Emergence of cooperative bistability and robustness of gene regulatory networks
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 16, Iss 6, p e1007969 (2020)
- Publication Year :
- 2020
- Publisher :
- Public Library of Science, 2020.
-
Abstract
- Gene regulatory networks (GRNs) are complex systems in which many genes regulate mutually to adapt the cell state to environmental conditions. In addition to function, the GRNs possess several kinds of robustness. This robustness means that systems do not lose their functionality when exposed to disturbances such as mutations or noise, and is widely observed at many levels in living systems. Both function and robustness have been acquired through evolution. In this respect, GRNs utilized in living systems are rare among all possible GRNs. In this study, we explored the fitness landscape of GRNs and investigated how robustness emerged in highly-fit GRNs. We considered a toy model of GRNs with one input gene and one output gene. The difference in the expression level of the output gene between two input states, “on” and “off”, was considered as fitness. Thus, the determination of the fitness of a GRN was based on how sensitively it responded to the input. We employed the multicanonical Monte Carlo method, which can sample GRNs randomly in a wide range of fitness levels, and classified the GRNs according to their fitness. As a result, the following properties were found: (1) Highly-fit GRNs exhibited bistability for intermediate input between “on” and “off”. This means that such GRNs responded to two input states by using different fixed points of dynamics. This bistability emerges necessarily as fitness increases. (2) These highly-fit GRNs were robust against noise because of their bistability. In other words, noise robustness is a byproduct of high fitness. (3) GRNs that were robust against mutations were not extremely rare among the highly-fit GRNs. This implies that mutational robustness is readily acquired through the evolutionary process. These properties are universal irrespective of the evolutionary pathway, because the results do not rely on evolutionary simulation.<br />Author summary Living systems have developed through a long history of Darwinian evolution. They acquired characteristic properties distinct from other physical systems; one is biological function. Another important property, which is overlooked by non-experts, is robustness to noise and mutation. Here, robustness means that a system does not lose its functionality when exposed to disturbances. Then, how do they relate to each other? In this paper, we explored this question using a toy model of gene regulatory networks (GRNs). While evolutionary simulations are usually used for such purposes, we instead generated GRNs randomly and classified them according to functionality. By requiring sensitive responses to environmental change as a function, we found that bistability emerges as a common property of highly-functional GRNs. Since this property does not depend on a particular evolutionary pathway, if the evolution was rewound and repeated over and over again, phenotypes with the same property would always evolve. At the same time, such bistable GRNs were robust to noise. We also found that GRNs robust to mutation were not extremely rare among the highly-functional GRNs. This implies that mutational robustness would be readily acquired through evolution.
- Subjects :
- 0301 basic medicine
Evolutionary Genetics
Bistability
Statistical methods
Computer science
Fitness landscape
Molecular Networks (q-bio.MN)
Gene regulatory network
Gene Expression
0302 clinical medicine
Electronics Engineering
Spectrum Analysis Techniques
Quantitative Biology - Molecular Networks
Gene Regulatory Networks
Biology (General)
Ecology
Statistics
near-Infrared Spectroscopy
Living systems
Monte Carlo method
Physical sciences
Computational Theory and Mathematics
Biological Physics (physics.bio-ph)
Modeling and Simulation
Probability distribution
Engineering and Technology
Biological system
Network Analysis
Research Article
Computer and Information Sciences
Evolutionary Processes
QH301-705.5
Complex system
FOS: Physical sciences
Infrared Spectroscopy
Network Motifs
Evolution, Molecular
03 medical and health sciences
Cellular and Molecular Neuroscience
Toggle Switches
Genetics
Gene Regulation
Physics - Biological Physics
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Condensed Matter - Statistical Mechanics
Evolutionary Biology
Statistical Mechanics (cond-mat.stat-mech)
Models, Genetic
Human evolutionary genetics
Robustness (evolution)
Biology and Life Sciences
Probability Theory
Probability Distribution
Research and analysis methods
030104 developmental biology
FOS: Biological sciences
Mutation
Mathematical and statistical techniques
030217 neurology & neurosurgery
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 16
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....9ddf8eac243a606821f0eed60b0e1c4e